aiqbench analysis
This paper addresses the critical gap between simulated benchmarks and real-world reliability for AI agents in physical environments like smart homes. By introducing SimVerity, the authors demonstrate that simulation success does not guarantee deployment safety, as sub-second failures often go undetected by standard checks but are exposed through independent physical witnesses. The work establishes a framework for explicit decision-making—clear, abstain, or escalate—that prioritizes auditability and risk profiling over blind trust in synthetic evaluations.
SimVerity: When Does Simulated Agent Success Survive Physical Deployment?
arXiv:2608.25067v1 Announce Type: new Abstract: Simulated evaluation is widely used to benchmark AI agents, yet how much evidence a simulated pass provides about physical deployment has not been systematically quantified. We present SimVerity, a verdict-transfer assurance framework: it replays matched scenarios on target smart home deployments and cross-validates agent execution against independently qualified physical witnesses. Our evaluation highlights that deployment success is a real-world process, not a static property in simulation: completion, reported state, observable effect, and settled outcome diverged within the same execution. Although an advanced simulator cleared all 240 light trials, a camera caught 42 sub-second failures invisible to settled-state checks. False clearance was predictable: a risk profile learned from measured trials and locked before evaluation predicted failures on a path it never physically measured, beating a property-blind baseline in all eleven held-out sessions across two cohorts. Agent auditability was also measurable: switching one agent loop's model-client/serving configuration raised its scenario-matching share from 52-88% to 100%. Finally, a second qualified simulator added no independent cross-check: it never disagreed on any overlapping case, and only physical measurement exposed their shared blind spots. SimVerity turns verdict transfer into an explicit decision: clear, abstain, or escalate before deployment.
Title:SimVerity: When Does Simulated Agent Success Survive Physical Deployment?
Abstract:Simulated evaluation is widely used to benchmark AI agents, yet how much evidence a simulated pass provides about physical deployment has not been systematically quantified. We present SimVerity, a verdict-transfer assurance framework: it replays matched scenarios on target smart home deployments and cross-validates agent execution against independently qualified physical witnesses. Our evaluation highlights that deployment success is a real-world process, not a static property in simulation: completion, reported state, observable effect, and settled outcome diverged within the same execution. Although an advanced simulator cleared all 240 light trials, a camera caught 42 sub-second failures invisible to settled-state checks. False clearance was predictable: a risk profile learned from measured trials and locked before evaluation predicted failures on a path it never physically measured, beating a property-blind baseline in all eleven held-out sessions across two cohorts. Agent auditability was also measurable: switching one agent loop's model-client/serving configuration raised its scenario-matching share from 52-88% to 100%. Finally, a second qualified simulator added no independent cross-check: it never disagreed on any overlapping case, and only physical measurement exposed their shared blind spots. SimVerity turns verdict transfer into an explicit decision: clear, abstain, or escalate before deployment.
Artificial Intelligence (cs.AI)
arXiv:2608.25067 [cs.AI]
(or arXiv:2608.25067v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.25067 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 293 words
